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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1002/ima.22525
|2 doi
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|a pubmed24n1538.xml
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|a eng
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| 100 |
1 |
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|a Selvaraj, Deepika
|e verfasserin
|4 aut
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| 245 |
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|a An integrated feature frame work for automated segmentation of COVID-19 infection from lung CT images
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|a Date Revised 18.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a © 2020 Wiley Periodicals LLC.
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|a The novel coronavirus disease (SARS-CoV-2 or COVID-19) is spreading across the world and is affecting public health and the world economy. Artificial Intelligence (AI) can play a key role in enhancing COVID-19 detection. However, lung infection by COVID-19 is not quantifiable due to a lack of studies and the difficulty involved in the collection of large datasets. Segmentation is a preferred technique to quantify and contour the COVID-19 region on the lungs using computed tomography (CT) scan images. To address the dataset problem, we propose a deep neural network (DNN) model trained on a limited dataset where features are selected using a region-specific approach. Specifically, we apply the Zernike moment (ZM) and gray level co-occurrence matrix (GLCM) to extract the unique shape and texture features. The feature vectors computed from these techniques enable segmentation that illustrates the severity of the COVID-19 infection. The proposed algorithm was compared with other existing state-of-the-art deep neural networks using the Radiopedia and COVID-19 CT Segmentation datasets presented specificity, sensitivity, sensitivity, mean absolute error (MAE), enhance-alignment measure (EMφ), and structure measure (S m) of 0.942, 0.701, 0.082, 0.867, and 0.783, respectively. The metrics demonstrate the performance of the model in quantifying the COVID-19 infection with limited datasets
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|a Journal Article
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|a Zernike moment
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|a artificial intelligence
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| 650 |
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|a computed tomography image
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| 650 |
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|a deep neural network
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| 650 |
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|a feature extraction
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| 650 |
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|a limited training points
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| 650 |
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4 |
|a segmentation
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| 700 |
1 |
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|a Venkatesan, Arunachalam
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Mahesh, Vijayalakshmi G V
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Joseph Raj, Alex Noel
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t International journal of imaging systems and technology
|d 1990
|g 31(2021), 1 vom: 01. März, Seite 28-46
|w (DE-627)NLM098193090
|x 0899-9457
|7 nnns
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| 773 |
1 |
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|g volume:31
|g year:2021
|g number:1
|g day:01
|g month:03
|g pages:28-46
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| 856 |
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|u http://dx.doi.org/10.1002/ima.22525
|3 Volltext
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